PROJECT SUMMARY Alzheimer's disease (AD) is the most common form of dementia with significant impact on patients, families and the public health system. An estimated 5.7 million Americans have Alzheimer's in 2018 1. At the time of clinical manifestation of the disease, significant irreversible brain damage is already present, rendering the diagnosis of AD at early stages of the disease an urgent prerequisite for potential therapies to delay or prevent symptoms2. It is estimated that early and accurate AD detection could save up to $7.9 trillion in medical and care costs1. Further, early AD detection and progression is crucial for monitoring the effect of experimental treatments as well as for informing developing efficient treatments. It is a pressing clinical need to improve early AD detection and progression. With recent advances in neurobiology of AD, our understanding of the disease has moved from one based on clinical symptoms to a biological construct that is multifactorial and heterogeneous and that cannot be explained by any single available biomarkers. NIH has devoted billions of dollars in the past decades to fund several centers and data initiatives on large cohorts of older adults; resulting in a wealth of multi-modal neuroimaging, cognitive, clinical, biospecimen, and genetic data. However, less effort has been made to implement innovative integrative methods for aggregating data across modalities to capture the heterogeneity of AD. To fill the gap in the analysis paradigm of multi-modal AD data, the overarching goals of the proposed study are to test and validate a multi-dimensional network framework for aggregating data across modalities in a single model to capture the heterogeneity of AD and to further enhance AD detection and progression. Our central hypothesis ? backed by previous evidence and preliminary data ? is that the proposed framework will enhance AD detection and progression by improving the ability to detect common as well as complementary signals across multiple data types and by reducing the effect of differences in scale, collection bias and noise in each modality. We will integrate behavioral, clinical, MR imaging, A? and Tau markers, and neurodegeneration markers from ADNI and Stanford ADRC data to test and validate the proposed multi-dimensional network framework for integration of different data types for early detection of AD (Aim1) as well as to characterize AD progression by applying multi-dimensional network framework to longitudinal changes in various measurements (Aim 2). To our knowledge, this is the first study that integrates various AD data in a multi-dimensional network model to characterize AD to further enhance AD detection and progression. If proven successful, this high-risk high-reward proposal will have a large impact on AD characterization, early detection and progression with significant health and economical impact. Moreover, successful completion of this study will provide critical tools for integrative analysis of multimodal data and will help shift the current analysis paradigm of available datasets and clinical trials which mainly focuses on independent analysis of single data types.